Ethnicity coding in health care

Health care is becoming more digitalised, and data driven. Precision medicine, clinical decision support systems and predictive analytics are no longer the future, but our present. Covid 19 has also accelerated the adoption of new technologies and new ways of working. For people like me with an interest in health informatics, these are exciting times. At the same time, for people like me who also have an interest in health inequalities, there is a sense of trepidation. As highlighted in the recently published paper “Pulse oximetry and racial bias”, technology can inadvertently be a contributing factor to health inequalities. Robust health care data with accurately coded ethnicity is essential in the development of these new technologies and in the fight to reduce health inequalities. Equally important is having the robust data at the appropriate level of granularity, analysed and interpreted in the right way.

As an example, I recently visited my optician to have my regular check-up which was long overdue. I noticed that the digital retinal photography machine was different. It was bigger and looked a lot more sophisticated. I was informed that this was in fact a new optical coherence tomography (OCT) machine. Long story short, the OCT scan gives the opticians a clearer idea of patient’s eye health. I was also informed that the OCT scan was an optional test which costs ten pounds. I asked my optician about the OCT scan and if it was worth the extra money I had just paid. During the explanation about how the OCT scan works, she mentioned something that piqued my interest. This OCT machine takes into account among other things, my ethnicity. I told her my job involved looking at race and health, with a specific focus on everything digital and data related. We had a really fascinating and insightful discussion about ethnicity, genetics, research and eyes. This was a real-life example about how medical data (including ethnicity) is being used in the development of new health care technologies. More importantly, how ethnicity is being used to inform and influence clinical decisions and delivery of care without us even knowing it.

Current state of ethnicity coding

The Nuffield Trust (supported by the NHS Race and Health Observatory) carried out research looking at ethnicity coding in English health service datasets. This report analysed Hospital Episode Statistics (HES) data (year range 2010/11 to 2019/20).

The research found out that,

• 87% of inpatient spells, 83% of outpatient and 86% of A&E attendances had a valid ethnic group recorded in 2019/20.
• It should be noted that “not stated” and “other” are valid and permitted codes, even though they are not useful for analysis purposes. As an example, for inpatients, 8.5% of records had a code of ‘not stated’ and 8.8% had an ‘other’ ethnic group.
• London had a high proportion of patients with ethnicity “not stated” or in the ‘other’ categories.
• The proportion of records with a valid ethnic group varied markedly between providers, from 53% to almost 100%
• People from an ethnic minority background disproportionately receive a different coding on different occasions that they encounter the NHS.

Even though “the proportion of health records containing the patient’s ethnicity code was high”, there is still work to do to improve the quality of ethnicity coding in health care records.

What can you do?

Accurate data with ethnicity coded correctly is key in identifying health outcome inequalities from an ethnicity perspective. If data is incomplete or not granular enough, key nuances can be missed. It is in the interest of everyone working in health care to make sure that the data and information they are working with has accurate ethnicity coding. The Nuffield Trust’s report has recommendations on what NHS organisations and Arm Length Bodies can do to help improve the quality of ethnicity coding in health care data sets.

Everyone working in health and social care can help to improve the quality of ethnicity coding in their organisations. From frontline clinicians to people working in supportive/administrative roles. Some of the things that individuals can do:

• Find out how your organisation is doing using NHS Digital’s “Current Data Quality Maturity Index (DQMI)” – https://digital.nhs.uk/data-and-information/data-tools-and-services/data-services/data-quality
• If you are involved in the creation of new medical records, make sure that the correct ethnicity code is used at that first contact with your service. Getting it right the first time should be the aspiration for all service.
• Use future contacts with patients as an opportunity to update ethnicity recording where this is not recorded appropriately.
• Share the Nuffield Trust report with colleagues and friends.